1
|
Salgado I, Prado Montes de Oca E, Chairez I, Figueroa-Yáñez L, Pereira-Santana A, Rivera Chávez A, Velázquez-Fernandez JB, Alvarado Parra T, Vallejo A. Deep Learning Techniques to Characterize the RPS28P7 Pseudogene and the Metazoa- SRP Gene as Drug Potential Targets in Pancreatic Cancer Patients. Biomedicines 2024; 12:395. [PMID: 38397997 PMCID: PMC11154313 DOI: 10.3390/biomedicines12020395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 11/15/2023] [Accepted: 11/21/2023] [Indexed: 02/25/2024] Open
Abstract
The molecular explanation about why some pancreatic cancer (PaCa) patients die early and others die later is poorly understood. This study aimed to discover potential novel markers and drug targets that could be useful to stratify and extend expected survival in prospective early-death patients. We deployed a deep learning algorithm and analyzed the gene copy number, gene expression, and protein expression data of death versus alive PaCa patients from the GDC cohort. The genes with higher relative amplification (copy number >4 times in the dead compared with the alive group) were EWSR1, FLT3, GPC3, HIF1A, HLF, and MEN1. The most highly up-regulated genes (>8.5-fold change) in the death group were RPL30, RPL37, RPS28P7, RPS11, Metazoa_SRP, CAPNS1, FN1, H3-3B, LCN2, and OAZ1. None of their corresponding proteins were up or down-regulated in the death group. The mRNA of the RPS28P7 pseudogene could act as ceRNA sponging the miRNA that was originally directed to the parental gene RPS28. We propose RPS28P7 mRNA as the most druggable target that can be modulated with small molecules or the RNA technology approach. These markers could be added as criteria to patient stratification in future PaCa drug trials, but further validation in the target populations is encouraged.
Collapse
Affiliation(s)
- Iván Salgado
- Medical Robotics and Biosignals Laboratory, Centro de Innovación y Desarrollo Tecnológico en Cómputo, Instituto Politécnico Nacional (IPN), Mexico City 07700, Mexico;
| | - Ernesto Prado Montes de Oca
- Regulatory SNPs Laboratory, Personalized Medicine National Laboratory (LAMPER), Guadalajara Unit, Medical and Pharmaceutical Biotechnology Department, Research Center in Technology and Design Assistance of Jalisco State (CIATEJ), National Council of Science and Technology (CONACYT), Guadalajara 44270, Jalisco, Mexico; (A.R.C.); (T.A.P.)
| | - Isaac Chairez
- Tecnologico de Monterrey, Institute of Advanced Materials for Sustainable Manufacturing, Monterrey 64849, Jalisco, Mexico;
| | - Luis Figueroa-Yáñez
- Industrial Biotechnology Unit, Center for Research and Assistance in Technology and Design of the State of Jalisco, A.C. (CIATEJ), Guadalajara 44270, Jalisco, Mexico; (L.F.-Y.); (A.P.-S.)
| | - Alejandro Pereira-Santana
- Industrial Biotechnology Unit, Center for Research and Assistance in Technology and Design of the State of Jalisco, A.C. (CIATEJ), Guadalajara 44270, Jalisco, Mexico; (L.F.-Y.); (A.P.-S.)
| | - Andrés Rivera Chávez
- Regulatory SNPs Laboratory, Personalized Medicine National Laboratory (LAMPER), Guadalajara Unit, Medical and Pharmaceutical Biotechnology Department, Research Center in Technology and Design Assistance of Jalisco State (CIATEJ), National Council of Science and Technology (CONACYT), Guadalajara 44270, Jalisco, Mexico; (A.R.C.); (T.A.P.)
| | | | - Teresa Alvarado Parra
- Regulatory SNPs Laboratory, Personalized Medicine National Laboratory (LAMPER), Guadalajara Unit, Medical and Pharmaceutical Biotechnology Department, Research Center in Technology and Design Assistance of Jalisco State (CIATEJ), National Council of Science and Technology (CONACYT), Guadalajara 44270, Jalisco, Mexico; (A.R.C.); (T.A.P.)
| | - Adriana Vallejo
- Unidad de Biotecnología Médica y Farmacéutica, CONACYT-Centro de Investigación y Asistencia en Tecnologia y Diseño del Estado de Jalisco AC, Av. Normalistas 800, Colinas de la Normal, Guadalajara 44270, Jalisco, Mexico
| |
Collapse
|
2
|
Salgado I, Prado Montes de Oca E, Chairez I, Figueroa-Yáñez L, Pereira-Santana A, Rivera Chávez A, Velázquez-Fernandez JB, Alvarado Parra T, Vallejo A. Deep Learning Techniques to Characterize the RPS28P7 Pseudogene and the Metazoa-SRP Gene as Drug Potential Targets in Pancreatic Cancer Patients. Biomedicines 2024; 12:395. [DOI: https:/doi.org/10.3390/biomedicines12020395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2024] Open
Abstract
The molecular explanation about why some pancreatic cancer (PaCa) patients die early and others die later is poorly understood. This study aimed to discover potential novel markers and drug targets that could be useful to stratify and extend expected survival in prospective early-death patients. We deployed a deep learning algorithm and analyzed the gene copy number, gene expression, and protein expression data of death versus alive PaCa patients from the GDC cohort. The genes with higher relative amplification (copy number >4 times in the dead compared with the alive group) were EWSR1, FLT3, GPC3, HIF1A, HLF, and MEN1. The most highly up-regulated genes (>8.5-fold change) in the death group were RPL30, RPL37, RPS28P7, RPS11, Metazoa_SRP, CAPNS1, FN1, H3−3B, LCN2, and OAZ1. None of their corresponding proteins were up or down-regulated in the death group. The mRNA of the RPS28P7 pseudogene could act as ceRNA sponging the miRNA that was originally directed to the parental gene RPS28. We propose RPS28P7 mRNA as the most druggable target that can be modulated with small molecules or the RNA technology approach. These markers could be added as criteria to patient stratification in future PaCa drug trials, but further validation in the target populations is encouraged.
Collapse
Affiliation(s)
- Iván Salgado
- Medical Robotics and Biosignals Laboratory, Centro de Innovación y Desarrollo Tecnológico en Cómputo, Instituto Politécnico Nacional (IPN), Mexico City 07700, Mexico
| | - Ernesto Prado Montes de Oca
- Regulatory SNPs Laboratory, Personalized Medicine National Laboratory (LAMPER), Guadalajara Unit, Medical and Pharmaceutical Biotechnology Department, Research Center in Technology and Design Assistance of Jalisco State (CIATEJ), National Council of Science and Technology (CONACYT), Guadalajara 44270, Jalisco, Mexico
| | - Isaac Chairez
- Tecnologico de Monterrey, Institute of Advanced Materials for Sustainable Manufacturing, Monterrey 64849, Jalisco, Mexico
| | - Luis Figueroa-Yáñez
- Industrial Biotechnology Unit, Center for Research and Assistance in Technology and Design of the State of Jalisco, A.C. (CIATEJ), Guadalajara 44270, Jalisco, Mexico
| | - Alejandro Pereira-Santana
- Industrial Biotechnology Unit, Center for Research and Assistance in Technology and Design of the State of Jalisco, A.C. (CIATEJ), Guadalajara 44270, Jalisco, Mexico
| | - Andrés Rivera Chávez
- Regulatory SNPs Laboratory, Personalized Medicine National Laboratory (LAMPER), Guadalajara Unit, Medical and Pharmaceutical Biotechnology Department, Research Center in Technology and Design Assistance of Jalisco State (CIATEJ), National Council of Science and Technology (CONACYT), Guadalajara 44270, Jalisco, Mexico
| | | | - Teresa Alvarado Parra
- Regulatory SNPs Laboratory, Personalized Medicine National Laboratory (LAMPER), Guadalajara Unit, Medical and Pharmaceutical Biotechnology Department, Research Center in Technology and Design Assistance of Jalisco State (CIATEJ), National Council of Science and Technology (CONACYT), Guadalajara 44270, Jalisco, Mexico
| | - Adriana Vallejo
- Unidad de Biotecnología Médica y Farmacéutica, CONACYT-Centro de Investigación y Asistencia en Tecnologia y Diseño del Estado de Jalisco AC, Av. Normalistas 800, Colinas de la Normal, Guadalajara 44270, Jalisco, Mexico
| |
Collapse
|
3
|
Yang M, Su Y, Xu K, Zheng H, Yuan Q, Cai Y, Aihaiti Y, Xu P. Ferroptosis-related lncRNAs guiding osteosarcoma prognosis and immune microenvironment. J Orthop Surg Res 2023; 18:787. [PMID: 37858131 PMCID: PMC10588205 DOI: 10.1186/s13018-023-04286-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Accepted: 10/14/2023] [Indexed: 10/21/2023] Open
Abstract
OBJECTIVE To investigate the ferroptosis-related long non-coding RNAs (FRLncs) implicated in influencing the prognostic and immune microenvironment in osteosarcoma (OS), and to establish a foundational framework for informing clinical decision making pertaining to OS management. METHODS Transcriptome data and clinical data pertaining to 86 cases of OS, the GSE19276, GSE16088 and GSE33382 datasets, and a list of ferroptosis-related genes (FRGs) were used to establish a risk prognostic model through comprehensive analysis. The identification of OS-related differentially expressed FRGs was achieved through an integrated analysis encompassing the aforementioned 86 OS transcriptome data and the GSE19276, GSE16088 and GSE33382 datasets. Concurrently, OS-related FRLncs were ascertained via co-expression analysis. To establish a risk prognostic model for OS, Univariate Cox regression analysis and Lasso Cox regression analysis were employed. Subsequently, a comprehensive evaluation was conducted, comprising risk curve analysis, survival analysis, receiver operating characteristic curve analysis and independent prognosis analysis. Model validation with distinct clinical subgroups was performed to assess the applicability of the risk prognostic model to diverse patient categories. Moreover, single sample gene set enrichment analysis (ssGSEA) was conducted to investigate variations in immune cell populations and immune functions within the context of the risk prognostic model. Furthermore, an analysis of immune checkpoint differentials yielded insights into immune checkpoint-related genes linked to OS prognosis. Finally, the risk prognosis model was verified by dividing the samples into train group and test group. RESULTS We identified a set of seven FRLncs that exhibit potential as prognostic markers and influence factors of the immune microenvironment in the context of OS. This ensemble encompasses three high-risk FRLncs, denoted as APTR, AC105914.2 and AL139246.5, alongside four low-risk FRLncs, designated as DSCR8, LOH12CR2, AC027307.2 and AC025048.2. Furthermore, our analysis revealed notable down-regulation in the high-risk group across four distinct immune cell types, namely neutrophils, natural killer cells, plasmacytoid dendritic cells and tumor-infiltrating lymphocytes. This down-regulation was also reflected in four key immune functions, antigen-presenting cell (APC)-co-stimulation, checkpoint, cytolytic activity and T cell co-inhibition. Additionally, we identified seven immune checkpoint-associated genes with significant implications for OS prognosis, including CD200R1, HAVCR2, LGALS9, CD27, LAIR1, LAG3 and TNFSF4. CONCLUSION The findings of this study have identified FRLncs capable of influencing OS prognosis and immune microenvironment, as well as immune checkpoint-related genes that are linked to OS prognosis. These discoveries establish a substantive foundation for further investigations into OS survival and offer valuable insights for informing clinical decision making in this context.
Collapse
Affiliation(s)
- Mingyi Yang
- Department of Joint Surgery, HongHui Hospital, Xi'an Jiaotong University, Xi'an, 710054, Shaanxi, China
| | - Yani Su
- Department of Joint Surgery, HongHui Hospital, Xi'an Jiaotong University, Xi'an, 710054, Shaanxi, China
| | - Ke Xu
- Department of Joint Surgery, HongHui Hospital, Xi'an Jiaotong University, Xi'an, 710054, Shaanxi, China
| | - Haishi Zheng
- Department of Joint Surgery, HongHui Hospital, Xi'an Jiaotong University, Xi'an, 710054, Shaanxi, China
| | - Qiling Yuan
- Department of Joint Surgery, HongHui Hospital, Xi'an Jiaotong University, Xi'an, 710054, Shaanxi, China
| | - Yongsong Cai
- Department of Joint Surgery, HongHui Hospital, Xi'an Jiaotong University, Xi'an, 710054, Shaanxi, China
| | - Yirixiati Aihaiti
- Department of Joint Surgery, HongHui Hospital, Xi'an Jiaotong University, Xi'an, 710054, Shaanxi, China
| | - Peng Xu
- Department of Joint Surgery, HongHui Hospital, Xi'an Jiaotong University, Xi'an, 710054, Shaanxi, China.
| |
Collapse
|